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RE: st: RE: Quantile regression with stata
Tomas M <firstname.lastname@example.org>
RE: st: RE: Quantile regression with stata
Fri, 20 Feb 2009 09:26:58 -0800
Thank you, thank you, thank you.
I am amazed at the quality of responses, it is like having a personal tutor with Stata.
Thank you everyone for your input and help.
> Subject: st: RE: Quantile regression with stata
> Date: Fri, 20 Feb 2009 12:51:04 +0000
> From: email@example.com
> To: firstname.lastname@example.org
> Some further comments embedded below.
> Tomas M
> I am using quantile regression to model the 50th percentile for my data.
> Unfortunately, the resources are limited on qreg when comparing to the
> literature available for traditional regression models.
> 1. I am mainly focused on the 50th percentile. But, if I wanted to
> compare 25th and 75th models (using the sreg with q(0.25 0.50 0.75)
> option), I am wondering if it is better to use the same set of
> predictors for each percentile, or if I should use a different set of
> predictors for each percentile? I wonder about this since each
> percentile may have a different set of significant predictors (for
> example, age may be significant for the 50th percentile, but not
> significant for the 25th percentile). Thus, is it better to compare
> models for 25th 50th and 75th percentiles using the best fitted model
> with all relevant significant predictors?
>>>> This is general modelling strategy and not specific to -qreg-. If
> you were say a graduate student of mine I'd personally rather see the
> same set of predictors being used in all models to be compared.
> Otherwise it's a matter of speculation how predictors left out of a
> model would have performed if included. I don't see that a model need
> include only predictors individually declared significant: that's
> putting more reliance on the machinery than is deserved. However, I
> would have no objection to also seeing slimmed down models. Other styles
> and tastes prevail too.
> 2. My other question pertains to interpretation of coefficients. When
> I run a model with certain predictors, sometimes I get a very small
> coefficient (i.e. 5e-15). How do I interpret this, and what does this
> mean? I do notice that this disappears once I collapse the categories
> for the predictor.
>>>> As above. It means as much or as little as it says. Without further
> evidence, it looks small, but all depends on what the units of
> measurement are and on looking at t-statistics as well and on
> considering what else in the model. If some categorical variable is
> represented by a bunch of indicators there is a very good case for
> keeping them all even if some aren't significant.
> 3. What tools are available to assess goodness of fit for my qreg model?
> I have read through the qreg postestimation commands for stata, and it
> seems that linktest, and predict would be my only options (i.e. plots of
> residuals versus fitted values are available). I have also looked
> through the UCLA regression with stata web book section on quantile
> regression, and it also states that there are limited postestimation
> commands available.
>>>> That is part illusion. You need not be restricted to canned
> commands. Indeed if you can get residuals and fitted you can get many
> other things too. Note that the -modeldiag- package (-search- for
> locations) includes several graphical commands that both make sense and
> work after -qreg-.
> 4. This final question relates to question 3. What would be the best
> method for variable selection for my final model? Still would be
> backwards elimination? How would I do this in stata, given the limited
> availability of post estimation commmands? Just start with all
> variables in my model, then eliminate ones with p-value greater than
> 0.05 (or add ones with p-value less than 0.05 if I were to add stepwise
> procedures too)?
>>>> The usual meta-comment is now that you won't get much support for
> any flavour of stepwise on this list. Search the list archives for
> "stepwise" and "Frank Harrell" for more.
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